OpenRaiser/Intern-Atlas huggingface dataset:
Novel Approaches for Predicting Risk Factors of Atherosclerosis
Venue: arXiv cs.LG 2015
Year: 2015

Coronary heart disease (CHD) caused by hardening of artery walls due to cholesterol known as atherosclerosis is responsible for large number of deaths world-wide. The disease progression is slow, asymptomatic and may lead to sudden cardiac arrest, stroke or myocardial infraction. Presently, imaging techniques are being employed to understand the molecular and metabolic activity of atherosclerotic plaques to estimate the risk. Though imaging methods are able to provide some information on plaque metabolism they lack the required resolution and sensitivity for detection. In this paper we consider the clinical observations and habits of individuals for predicting the risk factors of CHD. The identification of risk factors helps in stratifying patients for further intensive tests such as nuclear imaging or coronary angiography. We present a novel approach for predicting the risk factors of atherosclerosis with an in-built imputation algorithm and particle swarm optimization (PSO). We compare the performance of our methodology with other machine learning techniques on STULONG dataset which is based on longitudinal study of middle aged individuals lasting for twenty years. Our methodology powered by PSO search has identified physical inactivity as one of the risk factor for the onset of atherosclerosis in addition to other already known factors. The decision rules extracted by our methodology are able to predict the risk factors with an accuracy of $99.73%$ which is higher than the accuracies obtained by application of the state-of-the-art machine learning techniques presently being employed in the identification of atherosclerosis risk studies.
Iterative-Promoting Variable Step Size Least Mean Square Algorithm for Accelerating Adaptive Channel Estimation
Venue: arXiv cs.IT 2015
Year: 2015

Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergence speed and estimation performance. In this paper, we propose an iterative-promoting variable step size based least-mean-square error (VSS-LMS) algorithm to control the convergence speed as well as to improve the estimation performance. Simulation results show that the proposed algorithm can achieve better estimation performance than previous ISS-LMS while without sacrificing convergence speed.
Optimal Binary Locally Repairable Codes via Anticodes
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents a construction for several families of optimal binary locally repairable codes (LRCs) with small locality (2 and 3). This construction is based on various anticodes. It provides binary LRCs which attain the Cadambe-Mazumdar bound. Moreover, most of these codes are optimal with respect to the Griesmer bound.
On Partial Maximally-Recoverable and Maximally-Recoverable Codes
Venue: arXiv cs.IT 2015
Year: 2015

An [n, k] linear code C that is subject to locality constraints imposed by a parity check matrix H0 is said to be a maximally recoverable (MR) code if it can recover from any erasure pattern that some k-dimensional subcode of the null space of H0 can recover from. The focus in this paper is on MR codes constrained to have all-symbol locality r. Given that it is challenging to construct MR codes having small field size, we present results in two directions. In the first, we relax the MR constraint and require only that apart from the requirement of being an optimum all-symbol locality code, the code must yield an MDS code when punctured in a single, specific pattern which ensures that each local code is punctured in precisely one coordinate and that no two local codes share the same punctured coordinate. We term these codes as partially maximally recoverable (PMR) codes. We provide a simple construction for high-rate PMR codes and then provide a general, promising approach that needs further investigation. In the second direction, we present three constructions of MR codes with improved parameters, primarily the size of the finite field employed in the construction
On Kalman-Like Finite Impulse Response Filters
Venue: arXiv cs.SY 2015
Year: 2015

This note reveals an explicit relationship between two representative finite impulse response (FIR) filters, i.e. the newly derived and popularized Kalman-Like unbiased FIR filter (UFIR) and the receding horizon Kalman FIR filter (RHKF). It is pointed out that the only difference of the two algorithms lies in the noise statistics ignorance and appropriate initial condition construction strategy in UFIR. The revelation can benefit the performance improvement of one by drawing lessons from the other. Some interesting conclusions have also been drawn and discussed from this revelation.
A Data Annotation Architecture for Semantic Applications in Virtualized Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.
Everyday the Same Picture: Popularity and Content Diversity
Venue: arXiv cs.SI 2015
Year: 2015

Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than $40K$ followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about $2M$ likes and $190K$ comments, made by approximately $340K$ and $65K$ users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents.
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions
Venue: arXiv cs.NA 2015
Year: 2015

We consider the problem of optimizing an approximately convex function over a bounded convex set in $\mathbb{R}^n$ using only function evaluations. The problem is reduced to sampling from an \emph{approximately} log-concave distribution using the Hit-and-Run method, which is shown to have the same $\mathcal{O}^*$ complexity as sampling from log-concave distributions. In addition to extend the analysis for log-concave distributions to approximate log-concave distributions, the implementation of the 1-dimensional sampler of the Hit-and-Run walk requires new methods and analysis. The algorithm then is based on simulated annealing which does not relies on first order conditions which makes it essentially immune to local minima. We then apply the method to different motivating problems. In the context of zeroth order stochastic convex optimization, the proposed method produces an $ε$-minimizer after $\mathcal{O}^*(n^{7.5}ε^{-2})$ noisy function evaluations by inducing a $\mathcal{O}(ε/n)$-approximately log concave distribution. We also consider in detail the case when the "amount of non-convexity" decays towards the optimum of the function. Other applications of the method discussed in this work include private computation of empirical risk minimizers, two-stage stochastic programming, and approximate dynamic programming for online learning.
Generalized Simplified Variable-Scaled Min Sum LDPC decoder for irregular LDPC Codes
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose a novel low complexity scaling strategy of min-sum decoding algorithm for irregular LDPC codes. In the proposed method, we generalize our previously proposed simplified Variable Scaled Min-Sum (SVS-min-sum) by replacing the sub-optimal starting value and heuristic update for the scaling factor sequence by optimized values. Density evolution and Nelder-Mead optimization are used offline, prior to the decoding, to obtain the optimal starting point and per iteration updating step size for the scaling factor sequence of the proposed scaling strategy. The optimization of these parameters proves to be of noticeable positive impact on the decoding performance. We used different DVB-T2 LDPC codes in our simulation. Simulation results show the superior performance (in both WER and latency) of the proposed algorithm to other Min-Sum based algorithms. In addition to that, generalized SVS-min-sum algorithm has very close performance to LLR-SPA with much lower complexity.
Performance Tuning of a Parallel 3-D FFT Package OpenFFT
Venue: arXiv cs.MS 2015
Year: 2015

The fast Fourier transform (FFT) is a primitive kernel in numerous fields of science and engineering. OpenFFT is an open-source parallel package for 3-D FFTs, built on a communication-optimal domain decomposition method for achieving minimal volume of communication. In this paper, we analyze and tune the performance of OpenFFT, paying a particular attention to tuning of communication that dominates the run time of large-scale calculations. We first analyze its performance on different machines for an understanding of the behaviors of the package and machines. Based on the performance analysis, we develop six communication methods for performing communication with the aim of covering varied calculation scales on a variety of computational platforms. OpenFFT is then augmented with an auto-tuning of communication to select the best method in run time depending on their performance. Numerical results demonstrate that the optimized OpenFFT is able to deliver relatively good performance in comparison with other state-of-the-art packages at different computational scales on a number of parallel machines.
Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a method for learning And-Or models to represent context and occlusion for car detection and viewpoint estimation. The learned And-Or model represents car-to-car context and occlusion configurations at three levels: (i) spatially-aligned cars, (ii) single car under different occlusion configurations, and (iii) a small number of parts. The And-Or model embeds a grammar for representing large structural and appearance variations in a reconfigurable hierarchy. The learning process consists of two stages in a weakly supervised way (i.e., only bounding boxes of single cars are annotated). Firstly, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on car 3D CAD simulation. The And-Or model is organized in a directed and acyclic graph which can be inferred by Dynamic Programming. Secondly, the model parameters (for appearance, deformation and bias) are jointly trained using Weak-Label Structural SVM. In experiments, we test our model on four car detection datasets --- the KITTI dataset \cite{Geiger12}, the PASCAL VOC2007 car dataset~\cite{pascal}, and two self-collected car datasets, namely the Street-Parking car dataset and the Parking-Lot car dataset, and three datasets for car viewpoint estimation --- the PASCAL VOC2006 car dataset~\cite{pascal}, the 3D car dataset~\cite{savarese}, and the PASCAL3D+ car dataset~\cite{xiang_wacv14}. Compared with state-of-the-art variants of deformable part-based models and other methods, our model achieves significant improvement consistently on the four detection datasets, and comparable performance on car viewpoint estimation.
Particle swarm optimization for time series motif discovery
Venue: arXiv cs.LG 2015
Year: 2015

Efficiently finding similar segments or motifs in time series data is a fundamental task that, due to the ubiquity of these data, is present in a wide range of domains and situations. Because of this, countless solutions have been devised but, to date, none of them seems to be fully satisfactory and flexible. In this article, we propose an innovative standpoint and present a solution coming from it: an anytime multimodal optimization algorithm for time series motif discovery based on particle swarms. By considering data from a variety of domains, we show that this solution is extremely competitive when compared to the state-of-the-art, obtaining comparable motifs in considerably less time using minimal memory. In addition, we show that it is robust to different implementation choices and see that it offers an unprecedented degree of flexibility with regard to the task. All these qualities make the presented solution stand out as one of the most prominent candidates for motif discovery in long time series streams. Besides, we believe the proposed standpoint can be exploited in further time series analysis and mining tasks, widening the scope of research and potentially yielding novel effective solutions.
Negacyclic codes of odd length over the ring $\mathbb{F}_p[u,v]/\langle u^2,v^2,uv-vu\rangle$
Venue: arXiv cs.IT 2015
Year: 2015

We discuss the structure of negacyclic codes of odd length over the ring $\mathbb{F}_p[u, v]/ \langle u^2, v^2, uv-vu \rangle$. We find the unique generating set, the rank and the minimum distance for these negacyclic codes.
Weakly Supervised Learning for Salient Object Detection
Venue: arXiv cs.CV 2015
Year: 2015

Recent advances in supervised salient object detection has resulted in significant performance on benchmark datasets. Training such models, however, requires expensive pixel-wise annotations of salient objects. Moreover, many existing salient object detection models assume that at least one salient object exists in the input image. Such an assumption often leads to less appealing saliency maps on the background images, which contain no salient object at all. To avoid the requirement of expensive pixel-wise salient region annotations, in this paper, we study weakly supervised learning approaches for salient object detection. Given a set of background images and salient object images, we propose a solution toward jointly addressing the salient object existence and detection tasks. We adopt the latent SVM framework and formulate the two problems together in a single integrated objective function: saliency labels of superpixels are modeled as hidden variables and involved in a classification term conditioned to the salient object existence variable, which in turn depends on both global image and regional saliency features and saliency label assignment. Experimental results on benchmark datasets validate the effectiveness of our proposed approach.
Counting Homomorphisms to Square-Free Graphs, Modulo 2
Venue: arXiv cs.CC 2015
Year: 2015

We study the problem HomsTo$H$ of counting, modulo 2, the homomorphisms from an input graph to a fixed undirected graph $H$. A characteristic feature of modular counting is that cancellations make wider classes of instances tractable than is the case for exact (non-modular) counting, so subtle dichotomy theorems can arise. We show the following dichotomy: for any $H$ that contains no 4-cycles, HomsTo$H$ is either in polynomial time or is $\oplus P$-complete. This confirms a conjecture of Faben and Jerrum that was previously only known to hold for trees and for a restricted class of treewidth-2 graphs called cactus graphs. We confirm the conjecture for a rich class of graphs including graphs of unbounded treewidth. In particular, we focus on square-free graphs, which are graphs without 4-cycles. These graphs arise frequently in combinatorics, for example in connection with the strong perfect graph theorem and in certain graph algorithms. Previous dichotomy theorems required the graph to be tree-like so that tree-like decompositions could be exploited in the proof. We prove the conjecture for a much richer class of graphs by adopting a much more general approach.
Representing Objects, Relations, and Sequences
Venue: arXiv cs.LG 2015
Year: 2015

Vector Symbolic Architectures (VSAs) are high-dimensional vector representations of objects (eg., words, image parts), relations (eg., sentence structures), and sequences for use with machine learning algorithms. They consist of a vector addition operator for representing a collection of unordered objects, a Binding operator for associating groups of objects, and a methodology for encoding complex structures. We first develop Constraints that machine learning imposes upon VSAs: for example, similar structures must be represented by similar vectors. The constraints suggest that current VSAs should represent phrases ("The smart Brazilian girl") by binding sums of terms, in addition to simply binding the terms directly. We show that matrix multiplication can be used as the binding operator for a VSA, and that matrix elements can be chosen at random. A consequence for living systems is that binding is mathematically possible without the need to specify, in advance, precise neuron-to-neuron connection properties for large numbers of synapses. A VSA that incorporates these ideas, MBAT (Matrix Binding of Additive Terms), is described that satisfies all Constraints. With respect to machine learning, for some types of problems appropriate VSA representations permit us to prove learnability, rather than relying on simulations. We also propose dividing machine (and neural) learning and representation into three Stages, with differing roles for learning in each stage. For neural modeling, we give "representational reasons" for nervous systems to have many recurrent connections, as well as for the importance of phrases in language processing. Sizing simulations and analyses suggest that VSAs in general, and MBAT in particular, are ready for real-world applications.
Hyper-parameter optimization of Deep Convolutional Networks for object recognition
Venue: arXiv cs.CV 2015
Year: 2015

Recently sequential model based optimization (SMBO) has emerged as a promising hyper-parameter optimization strategy in machine learning. In this work, we investigate SMBO to identify architecture hyper-parameters of deep convolution networks (DCNs) object recognition. We propose a simple SMBO strategy that starts from a set of random initial DCN architectures to generate new architectures, which on training perform well on a given dataset. Using the proposed SMBO strategy we are able to identify a number of DCN architectures that produce results that are comparable to state-of-the-art results on object recognition benchmarks.
Live Group Detection for Mobile Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

This paper deals with distributed algorithms for monitoring the topology of a dynamic group of mobile wireless sensor networks. We propose two major extensions of a distributed static group consensus algorithm and an experimental implementation. Group consensus algorithms are exploited to let each node obtain the knowledge of its connected com-ponents. The proposed extensions provide a more accurate information about the proximity of nodes and allow to deal with dynamic networks using a periodical reevaluation of the group detection. We validate these algorithms by implementing them in an original and challenging application scenario, in the context of a real bicycle race. The real traces thus obtained and analyzed show the effectiveness of our live group detection implementation.
A hybrid TIM-NOMA scheme for the SISO Broadcast Channel
Venue: arXiv cs.IT 2015
Year: 2015

Future mobile communication networks will require enhanced network efficiency and reduced system overhead due to their user density and high data rate demanding applications of the mobile devices. Research on Blind Interference Alignment (BIA) and Topological Interference Management (TIM) has shown that optimal Degrees of Freedom (DoF) can be achieved, in the absence of Channel State Information (CSI) at the transmitters, reducing the network's overhead. Moreover, the recently emerged Non-Orthogonal Multiple Access (NOMA) scheme suggests a different multiple access approach, compared to the current orthogonal methods employed in 4G networks, resulting in high capacity gains. Our contribution is a hybrid TIM-NOMA scheme in Single-Input-Single-Output (SISO) K-user cells, in which users are divided into T groups, and 1/T DoF is achieved for each user. By superimposing users in the power domain, we introduce a two-stage decoding process, managing 'inter-group' interference based on the TIM principles, and 'intra-group' interference based on Successful Interference Cancellation (SIC), as proposed by NOMA. We show that for high SNR values the hybrid scheme can improve the sum rate by at least 100% when compared to Time Division Multiple Access (TDMA).
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
Venue: arXiv cs.CV 2015
Year: 2015

One issue limiting the adaption of large-scale multi-region segmentation is the sometimes prohibitive memory requirements. This is especially troubling considering advances in massively parallel computing and commercial graphics processing units because of their already limited memory compared to the current random access memory used in more traditional computation. To address this issue in the field of continuous max-flow segmentation, we have developed a \textit{pseudo-flow} framework using the theory of Bregman proximal projections and entropic distances which implicitly represents flow variables between labels and designated source and sink nodes. This reduces the memory requirements for max-flow segmentation by approximately 20\% for Potts models and approximately 30\% for hierarchical max-flow (HMF) and directed acyclic graph max-flow (DAGMF) models. This represents a great improvement in the state-of-the-art in max-flow segmentation, allowing for much larger problems to be addressed and accelerated using commercially available graphics processing hardware.
A Criticism of the Current Security, Privacy and Accountability Issues in Electronic Health Records
Venue: arXiv cs.CR 2015
Year: 2015

Cryptography has been widely accepted for security and partly for privacy control as discovered from past works. However, many of these works did not provide a way to manage cryptographic keys effectively especially in EHR applications, as this is the Achilles heel of cryptographic techniques currently proposed. The issue of accountability for legitimate users also has not been so popular and only a few considered it in EHR. Unless a different approach is used, the reliant on cryptography and password or escrow based system for key management will impede trust of the system and hence its acceptability. Also users with right access should also be monitored without affecting the clinician workflow. This paper presents a detailed review of some selected recent approaches to ensuring security, privacy and accountability in EHR and gaps for future research were also identified.